Data & Analytics Expert

Job Description At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection. The Data Expert provides technical leadership in designing and delivering scalable, reliable, and well‑governed enterprise data solutions. This role defines data architecture and engineering standards, solves complex data challenges, and drives modernization and engineering excellence across teams. As a Data Expert, you will provide technical leadership in the design, engineering, governance, and evolution of enterprise data solutions. This role serves as a senior technical authority for complex data initiatives, translating business and analytical needs into scalable, secure, reliable, and well‑governed data products. You will lead the design of modern data architectures and engineering patterns, establish technical standards and best practices, and solve highly complex data problems spanning multiple systems and business domains. You will partner with product managers, architects, engineers, analytics teams, and business stakeholders to modernize data platforms and create trusted, reusable data capabilities. You are expected to operate with a high degree of autonomy, influence technical direction across teams, mentor engineers, and drive continuous improvement in data architecture, engineering, governance, quality, observability, and operational excellence. Key Responsibilities Lead the architecture and design of complex, enterprise‑scale data solutions across cloud, lakehouse, warehouse, streaming, and analytical platforms. Define scalable architectural patterns for data ingestion, transformation, storage, integration, consumption, and lifecycle management. Provide technical direction for complex initiatives involving multiple applications, data domains, platforms, and engineering teams. Establish standards for data modeling, interoperability, metadata, lineage, security, performance, and platform integration. Lead technical design reviews and evaluate architectural trade‑offs involving scalability, reliability, maintainability, performance, security, and cost. Serve as a technical escalation point for complex data architecture, engineering, integration, and production challenges. Guide modernization of legacy data platforms and workloads using modern cloud and distributed data technologies. Design and implement highly scalable batch, streaming, and event‑driven data processing solutions. Develop complex data solutions using Python, PySpark, Scala, SQL, or comparable engineering languages. Apply distributed processing technologies such as Apache Spark, Kafka, Flink, dbt, or equivalent modern data frameworks. Design reusable engineering frameworks and patterns that accelerate delivery across multiple data products and teams. Design dimensional, relational, lakehouse, and domain‑oriented data models appropriate for operational and analytical use cases. Optimize distributed workloads, SQL queries, storage organization, partitioning strategies, and data pipelines for performance, scalability, and cost. Establish advanced practices for automated testing, dependency management, orchestration, failure recovery, replay, and operational resilience. Provide technical leadership for modern data solutions using Microsoft Fabric, including Lakehouse, Warehouse, OneLake, Data Pipelines, Notebooks, Dataflows Gen2, and Semantic Models. Design scalable processing solutions using Fabric Notebooks, Spark, PySpark, Spark SQL, and Delta Lake. Define patterns for data organization, ingestion, transformation, orchestration, and consumption within Microsoft Fabric. Apply appropriate Azure and/or AWS cloud services to enterprise data architectures. Evaluate emerging platform capabilities and determine where they can improve engineering productivity, scalability, reliability, and cost efficiency. Define and promote software engineering standards for data development, including Git‑based version control, code review, automated testing, CI/CD, and automated deployment. Establish reusable development patterns, coding standards, testing strategies, and deployment practices. Design observability strategies covering pipeline execution, data quality, dependencies, application health, performance, and failures. Drive proactive monitoring and operational excellence using platforms such as Datadog or equivalent observability technologies. Lead root‑cause analysis for complex production issues and drive solutions that prevent recurrence. Establish measurable engineering practices that improve reliability, maintainability, performance, and supportability. Define and implement enterprise‑level practices for data quality, metadata management, lineage, retention, classification, and lifecycle management. Establish automated data validation, profiling, reconciliation, and quality monitoring practices. Partner with business owners, data custodians, governance teams, security teams, and architects to establish trusted and governed data products. Ensure solutions align with applicable privacy, security, regulatory, retention, and enterprise data governance requirements. Establish clear ownership and accountability for critical data elements and governed data products. Design and communicate standards for documenting data sources, transformations, lineage, business rules, and downstream dependencies. Evaluate new data technologies, architectural patterns, and frameworks and recommend adoption where they provide measurable value. Provide technical leadership for modernization initiatives and migration from legacy data ecosystems to modern cloud and lakehouse platforms. Identify opportunities to simplify architecture, reduce technical debt, improve development velocity, and increase reuse. Apply generative AI and agentic AI tools and patterns where appropriate to improve data engineering productivity, metadata management, data quality, and operational processes. Develop prototypes and reference implementations to validate emerging technologies and architecture patterns. Partner with senior business and technology leaders to translate complex business needs into long‑term data capabilities. Communicate complex technical topics, architectural decisions, risks, and trade‑offs to both technical and non‑technical audiences. Facilitate architectural and technical discussions across engineering, product, analytics, governance, and business teams. Influence technical direction across multiple teams without direct supervisory authority. Mentor engineers and help develop advanced engineering and architecture capabilities across the organization. Create technical documentation, reference architectures, standards, and reusable implementation guidance. Core Skills Expert‑level knowledge of data architecture, data engineering, distributed processing, data integration, and data modeling. Advanced proficiency in Python, PySpark, Scala, SQL, or comparable languages used for large‑scale data processing. Advanced understanding of Microsoft Fabric, including Lakehouse, Warehouse, OneLake, Notebooks, Data Pipelines, Dataflows Gen2, and Semantic Models. Strong knowledge of Delta Lake, lakehouse architecture, distributed computing, batch processing, and streaming architectures. Advanced SQL expertise, including query optimization and performance tuning. Strong understanding of relational, dimensional, NoSQL, and distributed data modeling techniques. Experience designing reusable frameworks and enterprise‑scale data processing patterns. Strong knowledge of cloud architecture and data services within Microsoft Azure and/or AWS. Advanced understanding of engineering practices including Git, CI/CD, automated testing, infrastructure/deployment automation, and release management. Experience with observability platforms such as Datadog and designing operational monitoring for enterprise data solutions. Strong understanding of data governance, metadata, lineage, data quality, security, privacy, and retention principles. Demonstrated ability to troubleshoot complex distributed data and production problems. Demonstrated ability to lead technical initiatives and influence engineering standards across multiple teams. Excellent communication skills, with the ability to communicate architecture and technical strategy to engineering teams, business partners, and senior leadership. Preferred Skills Experience designing large‑scale solutions on Microsoft Fabric and Azure. Experience modernizing legacy data warehouse, ETL, or enterprise data processing environments into cloud/lakehouse architectures. Experience developing metadata‑driven data processing and orchestration frameworks. Experience with event‑driven and real‑time data architectures. Experience establishing enterprise data engineering standards, reusable frameworks, or reference architectures. Experience with data cataloging, lineage, governance, and observability capabilities. Experience applying generative AI and agentic AI technologies to data engineering and operational use cases. Experience leading architecture reviews, proof‑of‑concepts, technology evaluations, and complex platform modernization efforts. Experience 10 or more years of relevant technology, data engineering, data architecture, or data platform experience (Preferred). Significant experience designing and delivering complex, enterprise‑scale data solutions. Demonstrated experience providing technical leadership across large or cross‑functional initiatives. Skills Data Analysis, Data Governance, Data Modeling, Data Pipelines, Enterprise Data, Technical Strategy Compensation Compensation Compensation offered for this role is 120,000.00 - 193,725.00 annually and is based on experience and qualifications. The candidate #J-18808-Ljbffr